Who owns what when AI creates everything? (nanobits.beehiiv.com)

As generative models produce text, images, code and music at scale, the core question — who owns what when AI creates everything? — has become urgent for creators, platforms, and policymakers. The article frames a rapidly intensifying debate: copyright systems generally require a human author, training data often contains copyrighted or proprietary material, and cloud APIs blur the line between model ownership, weights, and outputs. That ambiguity affects liability, royalties, and commercial use: artists and companies worry about unauthorized reuse of their work, while platform and model authors face exposure for training-set content and for outputs that reproduce proprietary material. Technically and legally, the split points are clear but messy: ownership of model weights (and the right to sell or license them) differs from ownership of individual outputs; memorization and model inversion create real risks of leaking training data; and license compatibility (open-source vs proprietary) constrains downstream uses. Practical mitigations include dataset provenance and documentation, differential privacy and clipping to reduce memorization, watermarking and fingerprinting outputs, and contractual licensing regimes or statutory reform. The stakes are high: clarity will shape business models for LLMs and image models, determine compensation for original creators, and guide regulation and standards (provenance, watermarking, and auditability) needed for scalable, compliant AI deployment.